Ascend.io

SEPTEMBER 6, 2024

AI-driven data quality workflows deploy machine learning to automate data cleansing, detect anomalies, and validate data. Integrating AI into data workflows ensures reliable data and enables smarter business decisions. Data quality is the backbone of successful data engineering projects.

Data Cleanse 52

Data Cleanse Data Workflow Data Pipeline Machine Learning 52

Databand.ai

AUGUST 30, 2023

A DataOps architecture is the structural foundation that supports the implementation of DataOps principles within an organization. It encompasses the systems, tools, and processes that enable businesses to manage their data more efficiently and effectively. As a result, they can be slow, inefficient, and prone to errors.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

phData: Data Engineering

APRIL 4, 2023

Today we want to introduce Fivetran’s support for Amazon S3 with Apache Iceberg, investigate some of the implications of this feature, and learn how it fits into the modern data architecture as a whole. Fivetran today announced support for Amazon Simple Storage Service (Amazon S3) with Apache Iceberg data lake format.

Data Lake 52

Data Lake Amazon Web Services Data Cleanse Data Warehouse 52

Knowledge Hut

JULY 5, 2024

Future Developments: Evolution towards serverless architectures, automated scaling, and tighter integration with advanced cloud-based analytics. Data Mesh Implementation: Overview: Data Mesh, a decentralized approach, is gaining traction for scalable and domain-oriented data architecture.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Cleanse 52

Databand.ai

JUNE 28, 2023

Data pipelines often involve a series of stages where data is collected, transformed, and stored. This might include processes like data extraction from different sources, data cleansing, data transformation (like aggregation), and loading the data into a database or a data warehouse.

Data Pipeline 98

Data Pipeline Data Engineer Data Engineering Engineering 98

Knowledge Hut

APRIL 20, 2023

Data Governance Examples Here are some examples of data governance in practice: Data quality control: Data governance involves implementing processes for ensuring that data is accurate, complete, and consistent. This may involve data validation, data cleansing, and data enrichment activities.

Data Governance 59

Data Governance Government Data Cleanse Data Security 59

Ascend.io

NOVEMBER 21, 2023

The emergence of cloud data warehouses, offering scalable and cost-effective data storage and processing capabilities, initiated a pivotal shift in data management methodologies. Read More: Zero ETL: What’s Behind the Hype?

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52